PROJECT SUMMARY/ABSTRACT Meningiomas are the most common primary brain tumor. The majority of meningiomas are Grade I, and assumed to be benign, yet 25-30% will recur within 5 years. Higher-grade meningiomas, including Grades II and III, are aggressive tumors that can recur, but it is unknown which tumors will recur, which require adjuvant radiation, and which do not. Meningiomas are treated primarily with surgical resection, and when they recur can be treated with additional resection or adjuvant radiation. There are no known medical therapies for meningiomas. Several recent whole-genome/whole-exome sequencing studies have been performed and identified a number of genes that are mutated in meningioma, most commonly NF2, and less frequently TRAF7, KLF4, and SMO. However, the clinical relevance of these mutations and their impact on tumor recurrence or aggressiveness remain unknown because the sequencing studies did not have well annotated clinical data to correlate with the gene mutations. In the first aim of this proposal, we will sequence Grade I (n=150), Grade II (n=125) and Grade III (n=43) meningioma samples from the UCSF Brain Tumor Research Center Tissue bank with a panel of 28 genes that are recurrently mutated in meningioma tumors. We will use a recursive partitioning statistical algorithm (partDSA) to test which gene mutations co-occur, and what combination of gene mutations provides a molecular signature that predicts patient prognosis using cross-validation techniques. We will use this to develop molecularly defined subgroups of meningioma that predict clinical outcomes, including time to recurrence, progression free survival, and overall survival. In the second aim of this proposal we will test whether a heritable single nucleotide polymorphism (SNP) in MLLT10, previously associated with meningioma risk in GWAS, is associated with a particular molecular or histological subgroup of meningioma. The results generated by this research will allow us to gain insights into what molecular mutations and genomic risk factors drive particular subgroups of meningioma and will allow the development of targeted, personalized, therapies to individual meningiomas based on their molecular profile. It will also provide prognostic information to patients and clinicians based on the molecular signature of individual meningiomas and allow personalized tailoring of therapy based on risk of tumor recurrence.